Twin Delayed Deep Deterministic Policy Gradient-Based Battery Cooling Strategy for Electric Vehicles

Temperature heavily affects the lifespan and performance of batteries. High temperatures accelerate capacity degradation and can cause thermal runaway, highlighting the importance of battery cooling strategies for electric vehicles. This paper proposes the battery cooling strategy for electric vehicles with LiFePO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> batteries using the twin delayed deep deterministic policy gradient (TD3) algorithm. Firstly, the electric-thermal-aging and active battery thermal management system models are introduced, and the thermal management problem is formulated as a continuous Markov decision process. Then, a reward function is designed based on prolonging battery life, reducing refrigeration cost, and maintaining battery temperature. Finally, a TD3 algorithm based on the double-delayed update mechanism is designed to obtain the optimal battery cooling strategy in the continuous state-action space. Simulation results demonstrate that the proposed strategy outperforms the traditional methods and closes to the optimal benchmark, offline dynamic programming, with battery capacity loss of less than 3.32% and an SoC consumption of less than 0.55%, significantly reducing the operational cost and mitigating the battery aging.